Job purpose:

We are looking for a Senior AI Leader to head the AI Practice at Datamatics, driving enterprise-wide AI strategy, innovation, and delivery.

This role will focus on leading teams in the design and implementation of AI-driven solutions, with a strong emphasis on Agentic AI, Retrieval-Augmented Generation (RAG), and enterprise-scale intelligent systems.

The ideal candidate will bring a strong leadership background, guiding architecture, governance, and adoption of cutting-edge AI technologies across business functions.

The ideal candidate will influence technology direction, shape presales strategy, and ensure alignment between AI innovation and business value realization.

Key responsibilities:

- Lead the AI Practice, defining the vision, roadmap, and strategy for AI adoption across the organization.

- Oversee solution architecture and delivery of AI initiatives leveraging LLMs, RAG frameworks, and enterprise data platforms.

- Provide thought leadership on emerging AI technologies, frameworks, and best practices.

- Establish frameworks, governance, and reusable components for scalable AI implementation.

- Establish AI architecture principles, standards, and governance frameworks across the organization.

- Drive development of reusable solution accelerators, reference architectures, and best practices.

- Build and mentor a strong AI and data science team, fostering innovation and collaboration.

- Represent the AI Practice in client discussions, steering PoCs and solution proposals for business impact.

Architecture & Solution:

- Oversee end-to-end design and delivery of AI solutions including agentic workflows, RAG systems, LLM-powered applications, and ML models integrated into enterprise platforms.

- Guide architecture reviews, technical design validations, and PoC direction for AI initiatives.

- Ensure alignment of AI solution design with cloud-native, microservices-based, and containerized deployment models.

- Partner with enterprise data and ML teams to align AI strategy with organizational goals and data maturity.

Innovation & Technology Leadership:

- Guide teams on leveraging tools such as LangChain, LlamaIndex, vector databases, and orchestration frameworks.

- Promote adoption of modern MLOps practices including lifecycle management, deployment, monitoring, and evaluation of AI models.

- Rapid innovation while ensuring enterprise-grade scalability and reliability.

Stakeholder & Business Engagement:

- Collaborate with CXOs, business heads, and technology leaders to identify, prioritize, and execute AI opportunities.

- Represent the AI Practice in client engagements, supporting solution proposals, PoCs, and RFP responses.

- Translate complex AI concepts into clear strategic insights for senior stakeholders.

CoE & Organizational Contribution:

- Contribute to the AI Center of Excellence by driving standardization, documentation, and knowledge sharing.

- Establish evaluation frameworks for model performance, risk, and responsible AI usage.

- Influence enterprise architecture decisions and cross-functional technology roadmaps.

Qualifications:

- Education: Graduate - B.E/B.Tech - IT/CS.

Requirements:

- Overall 20+ years of experience in IT.

- Proven experience in leading AI strategy, architecture, and delivery at an enterprise or practice level.

- Deep knowledge of AI solution design principles, prompt optimization, and enterprise AI deployment models.

- Exposure to at least one major cloud AI platform (AWS / Azure / GCP).

- Strong familiarity with MLOps concepts and enterprise data/AI platforms.

- Strong stakeholder management, communication, and cross-functional leadership skills.

- Demonstrated ability to translate emerging AI capabilities into actionable business outcomes.

- Strategic mindset with experience in building and scaling AI teams and practices.

Technical & Domain Knowledge:

- Strong understanding of LLM orchestration tools and frameworks.

- Familiarity with vector databases and LLM APIs (OpenAI, Azure OpenAI, HuggingFace, Cohere).

- Knowledge of AI solution design, prompt optimization, and deployment models.

- Awareness of ML/DL frameworks and data engineering ecosystems.

- Understanding of cloud-native and containerized environments for AI workloads.

Leadership & Behavioral Skills:

- Exceptional stakeholder management and executive communication ability.

- Strategic mindset with the ability to convert emerging AI capabilities into business impact.

- Track record of guiding cross-functional teams and influencing decisions.